Non-transitory computer-readable recording medium and charge calculation method

ABSTRACT

A charge calculation method executed by a processor included in a computer to execute a process, the process includes determining a degree of possibility that a virtual machine having redundant configuration exists in a plurality of virtual machines that provide a service, calculating a difference of charges for using the service before and after a first virtual machine among the plurality of virtual machines is migrated to an another location different from a location where the first virtual machine is located, and displaying the degree of possibility and the difference with respect to the service.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2020-045049 filed on Mar. 16,2020, the entire contents of which are incorporated herein by reference.

FIELD

A certain aspect of the embodiments is related to a non-transitorycomputer-readable recording medium and a charge calculation method.

BACKGROUND

With the development of virtualization technology, a cloud that providesvarious services to a user by using virtual machines running in a datacenter is widespread. In the cloud, since data and programs required forthe service are managed by the virtual machine, it is not necessary forthe user to manage the data and the programs, and it is possible toimprove the efficiency of user's business and reduce a cost.

In recent years, a new service that combines clouds from different cloudproviders is also proposed. In this service, each of a plurality ofvirtual machines is realized by the clouds of a plurality of differentcloud providers. Thereby, even if a failure occurs in any of the cloudproviders, the virtual machines can continue to operate in the cloud ofthe remaining cloud providers, so that the virtual machines can be maderedundant.

In such a service, some cloud providers may reduce the charge of theirown virtual machine. In that case, a usage charge of the service becomescheaper than a current charge by using the virtual machine after thecharge reduction. Therefore, it is preferable to encourage the users ofthe service to migrate their virtual machine to the virtual machines ofthe cloud provider that reduce the charge. Note that the techniquerelated to the present disclosure is disclosed in Japanese PatentApplication Publication No. 2017-142673.

SUMMARY

According to an aspect of the present disclosure, there is provided acharge calculation method executed by a processor included in a computerto execute a process, the process including: determining a degree ofpossibility that a virtual machine having redundant configuration existsin a plurality of virtual machines that provide a service; calculating adifference of charges for using the service before and after a firstvirtual machine among the plurality of virtual machines is migrated toan another location different from a location where the first virtualmachine is located; and displaying the degree of possibility and thedifference with respect to the service.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram illustrating a usage charge of a virtualmachine per unit time for each cloud provider;

FIG. 2 is a schematic diagram of services using virtual machinesdeployed at different cloud locations;

FIG. 3 is a schematic diagram illustrating a ratio of virtual machineshaving the same specifications as virtual machines in a cloud locationwhose usage charge is reduced, to virtual machines included in each ofcurrent services;

FIG. 4A is a schematic diagram of a virtual machine having redundantconfiguration;

FIG. 4B is a schematic diagram illustrating a case where the virtualmachine in a cloud location P is migrated to a cloud location R;

FIG. 5 is a system configuration diagram of a system according to apresent embodiment;

FIG. 6 is a schematic diagram of services using virtual machinesdeployed in cloud locations in the present embodiment;

FIG. 7A is a schematic diagram illustrating a determination methodaccording to a first example of the present embodiment;

FIG. 7B is a schematic diagram illustrating a monitoring result oftraffic volumes in the first example;

FIG. 8 is a schematic diagram illustrating a determination ruleaccording to the first example of the present embodiment;

FIG. 9A is a schematic diagram illustrating a determination methodaccording to a second example of the present embodiment;

FIG. 9B is a schematic diagram illustrating a monitoring result of thetraffic volumes in the second example;

FIG. 10 is a schematic diagram illustrating a determination ruleaccording to the second example of the present embodiment;

FIG. 11A is a schematic diagram illustrating a determination methodaccording to a third example of the present embodiment;

FIG. 11B is a schematic diagram illustrating a monitoring result of thetraffic volumes in the third example;

FIG. 12 is a schematic diagram illustrating a determination ruleaccording to the third example of the present embodiment;

FIG. 13A is a schematic diagram illustrating a determination methodaccording to a fourth example of the present embodiment;

FIG. 13B is a schematic diagram illustrating a monitoring result ofresource usage rates in the fourth example;

FIG. 14 is a schematic diagram illustrating a determination ruleaccording to the fourth example of the present embodiment;

FIG. 15A is a schematic diagram illustrating a determination methodaccording to a fifth example of the present embodiment;

FIG. 15B is a schematic diagram illustrating a monitoring result of thetraffic volumes in the fifth example;

FIG. 16 is a schematic diagram illustrating a determination ruleaccording to the fifth example of the present embodiment;

FIG. 17 is a functional configuration diagram of an informationprocessing device according to the present embodiment;

FIG. 18 is a diagram illustrating an example of virtual machineinformation according to the present embodiment;

FIG. 19 is a schematic diagram illustrating an example of deploymentdestinations of the virtual machines to be calculated in the presentembodiment.

FIG. 20 is a flowchart of a charge calculation method according to thepresent embodiment;

FIG. 21 is a schematic diagram illustrating a sorting method in thepresent embodiment;

FIG. 22A is a schematic diagram illustrating another sorting method inthe present embodiment;

FIG. 22B is a schematic diagram illustrating still another sortingmethod in the present embodiment;

FIG. 23 is a schematic diagram illustrating an example of screen displayof the display unit in the present embodiment;

FIG. 24 is a flowchart of a determination process according to the firstexample of the present embodiment;

FIG. 25 is a flowchart of a determination process according to thesecond example of the present embodiment;

FIG. 26 is a flowchart of a determination process according to the thirdexample of the present embodiment;

FIG. 27 is a flowchart of a determination process according to thefourth example of the present embodiment;

FIG. 28 is a flowchart of a determination process according to the fifthexample of the present embodiment; and

FIG. 29 is a hardware configuration diagram of the informationprocessing device according to the present embodiment.

DESCRIPTION OF EMBODIMENTS

For example, if respective redundant virtual machines are migrated tothe same cloud provider, the respective virtual machines cannot be usedin the case where the failure occurs in the cloud provider. Therefore,it makes no sense to make the virtual machines redundant. Moreover, whenthe user of the service voluntarily makes the virtual machinesredundant, it is difficult for an administrator of the service todetermine that each of the virtual machines has redundant configuration.When providing candidates for the virtual machines to be migrated to theuser of the service, if the above-mentioned virtual machines that areunlikely to be migrated by the user of the service are also included inthe candidates, this might prevent the user of the service fromselecting the virtual machine to be migrated.

Prior to the description of the present embodiment, matters studied byan inventor will be described.

As described above, in the service that combines the plurality of cloudservices, it is preferable to encourage the user to migrate a virtualmachine in use to the virtual machine of the cloud provider whose usagecharge is reduced, and to propose reduction in a usage charge of theservice to the user.

FIG. 1 is a schematic diagram illustrating the usage charge of thevirtual machine per unit time for each cloud provider.

As illustrated in FIG. 1, the usage charge of the virtual machine isdifferent for each of cloud providers X, Y, and Z. Furthermore, even ifthe cloud provider is the same, the usage charge of the virtual machinevaries depending on a region such as Japan, United Kingdom (UK), andUnited States of America (USA). The region is a place where a datacenter in which the virtual machines are deployed is installed.

Hereinafter, the combination of each of cloud providers X, Y, Z and theregion is referred to as a cloud location. The cloud locations can beexpressed as (X, Japan), (Y, UK), (Z, USA) or the like, for example.Furthermore, even if the cloud provider is the same, as indicated by (X,Japan) and (X, UK), the cloud locations may be different from eachother.

FIG. 2 is a schematic diagram of services A, B, C and D using virtualmachines deployed at different cloud locations P, Q and R. As anexample, it is assumed that the cloud locations are P=(X, Japan), Q=(Y,UK) and R=(Z, USA).

These services A, B, C and D are managed by a same administrator M. Theadministrator M appropriately selects a plurality of virtual machines VMfrom at least two of the cloud locations P, Q, and R, connects theplurality of virtual machines by a network suitable for the service, andprovides it to the user of the service.

When providing a service A to a user, for example, the administrator Mselects three virtual machines VM in the cloud location P, and selectsone virtual machine VM from each of the cloud locations Q and R. Then,the administrator M connects these virtual machines VM by an appropriatenetwork.

For example, it is assumed a case where the cloud provider of the cloudlocation R reduces the usage charge of a virtual machine VM0. In thiscase, it is preferable to migrate a virtual machine having the samespecifications as the virtual machine VM0 among the three virtualmachines VM of the cloud location P in the service A to the virtualmachine VM0 from a viewpoint of charge reduction. Here, thespecifications of the virtual machine VM that serves as the referencefor migration include the number of virtual CPUs, a capacity of avirtual memory and so on, in the virtual machine VM. This allows theadministrator M to reduce the usage charge to the user of service A, andthe cost of the service A can be reduced. The same applies to theservices B, C, and D.

How much cost reduction is possible depends on a ratio of virtualmachines VM having the same specifications as the virtual machine VM0 ofthe cloud location R whose usage charge is reduced, to the virtualmachines VM included in each of the current services A, B, C, and D.

FIG. 3 is a schematic diagram illustrating the rate. In an example ofFIG. 3, the service A has a maximum ratio of virtual machines having thesame specifications as the virtual machine VM0 in the cloud location Rto all the virtual machines.

Therefore, significant charge reduction can be realized by migrating avirtual machine having the same specifications as the virtual machineVM0 in the cloud location R among the virtual machines VM of service A,to the cloud location R.

However, when the service A includes a redundant virtual machine VM, itmay be difficult to migrate the virtual machine VM as follows.

FIG. 4A is a schematic diagram of a virtual machine having redundantconfiguration. In an example of FIG. 4A, one of the two virtual machinesVM having the same function belongs to the cloud location P, and theother belongs to the cloud location R. Thereby, even if the failureoccurs in the cloud location P, for example, the service A can beprovided by the virtual machine VM of the cloud location R, so that theavailability of the service A can be improved.

FIG. 4B is a schematic diagram illustrating a case where the virtualmachine in a cloud location P is migrated to the cloud location R.

When the charge of the virtual machine VM in the cloud location R isreduced, the usage charge of the service A can be reduced by migratingthe virtual machine VM in this way. Therefore, it is also consideredthat the administrator M of the service A calculates the charge of theservice A after the charge reduction and presents it to the user of theservice A.

However, in this case, both of the two virtual machines VM having theredundant configuration are deployed in the same cloud location R.Therefore, when the failure occurs in the cloud location R, the usercannot use the service A and the availability of service A is reduced.

Whether the two virtual machines VM have the redundant configuration isdetermined by application programs that the user of the service A runson respective virtual machines VM. When the respective virtual machinesVM run the same application programs, for example, the functions of therespective virtual machines VM are the same, and these virtual machinesVMs have the redundant configuration.

However, although the administrator M of the service A knows the networkconfiguration that connects the virtual machines VM to each other, theapplication programs that run on the respective virtual machines VM arenot included in a category managed by the administrator M. The user ofthe service A can voluntarily make the virtual machines VM redundant byrunning the same application programs on the respective virtual machinesVM. Therefore, the administrator M of the service A cannot directlydetermine whether each virtual machine VM has the redundantconfiguration.

Therefore, even if the user of the service A is encouraged to migratethe virtual machine VM to the cloud location R, it is unlikely that theuser of the service A accepts it, and the calculation of a new usagecharge becomes useless.

Hereinafter, each embodiment capable of eliminating such uselessness isdescribed.

Embodiment

FIG. 5 is a system configuration diagram of a system according to apresent embodiment. A system 10 calculates a difference in usage chargesfor using the service before and after migrating the virtual machine VMto another cloud location, in the service using the virtual machines VMdeployed in the cloud locations P, Q, and R. In this example, the system10 has an information processing device 12 connected to the cloudlocations P, Q, and R via a network 11. As described above, also in thepresent embodiment, the combination of any of the cloud providers X, Y,Z and the region is referred to as the cloud location P, Q or R. Thecloud locations are P=(X, Japan), Q=(Y, UK) and R=(Z, USA), for example.

The information processing device 12 is a computer such as a server or aPC (Personal Computer), and accesses charge information 15 of the cloudproviders X, Y, and Z via the network 11. The charge information 15 is aweb page in which charge tables of the virtual machine VM in therespective cloud locations P, Q, and R are described. The informationprocessing device 12 can detect whether the usage charge of the virtualmachine VM in each of the cloud locations P, Q, and R is reduced, byreferring to the charge information 15.

FIG. 6 is a schematic diagram of services A, B, C and D using virtualmachines deployed in cloud locations P, Q and R.

The administrator N of the system 10 described above manages the cloudlocations where the respective virtual machines VM in the services A, B,C, and D are deployed and the network for connecting the respectivevirtual machines VM.

However, the administrator N does not manage whether each virtualmachine VM has the redundant configuration. Furthermore, each user ofthe services A, B, C, and D can freely run the application programs onthe virtual machines VM, and the administrator N does not have anauthority to manage the application programs. Therefore, even if theuser runs the same application programs on the two virtual machines VMand makes these virtual machines VM redundant, the administrator Ncannot directly know the redundant configuration.

Therefore, in the present embodiment, the information processing device12 determines a possibility that the virtual machine VM has theredundant configuration as follows. The determination method of theredundant configuration includes the following first to fifth examples.

First Example

FIG. 7A is a schematic diagram illustrating a determination methodaccording to the first example. Hereinafter, a plurality of virtualmachines VM that provide service A are referred to as virtual machinesVM1, VM2, and VM3. The virtual machines VM1, VM2, and VM3 may bedeployed in the same cloud location, or may be deployed in differentcloud locations. Further, the services B, C, and D may be used insteadof the service A. This also applies to the second and third examplesdescribed later.

In this example, when there are two virtual machines VM1 and VM2 underthe control of a single virtual machine VM3, the information processingdevice 12 monitors traffic volumes tr₁ and tr₂ input to these virtualmachines VM1 and VM2, respectively. Note that the traffic volumes tr₁ isan example of a first traffic volume, and the traffic volumes tr₂ is anexample of a second traffic volume.

If the virtual machines VM1 and VM2 have the redundant configuration,the virtual machine VM3 has a high possibility to function as a loadbalancer that distributes traffic to the virtual machines VM1 and VM2.Therefore, there should be no large difference between the trafficvolumes tr₁ and tr₂.

Therefore, in this example, the information processing device 12monitors the traffic volumes tr₁ and tr₂ to determine a degree ofpossibility that the virtual machines VM1 and VM2 have the redundantconfiguration.

FIG. 7B is a schematic diagram illustrating a monitoring result of thetraffic volumes tr₁ and tr₂ in the first example.

In this example, total volumes (bits) of traffic in one minute are setas the traffic volumes tr₁ and tr₂, and the information processingdevice 12 calculates a difference Δtr_(a) between them. Then, theinformation processing device 12 determines the degree of possibility ofthe redundant configuration based on a ratio P_(t)(=Δtr_(a)/tr₁)occupied by the difference Δtr_(a) to the traffic amount tr₁.

FIG. 8 is a schematic diagram illustrating a determination rule R1 fordetermining the degree of possibility.

In the determination rule R1, the information processing device 12determines the degree of possibility depending on which of theconditions “R1-1”, “R1-2”, and “R1-3” is satisfied, as illustrated inFIG. 8.

Here, the information processing device 12 measures the traffic volumestr₁ and tr₂ 5 times. When the ratio P_(t) is less than 10% in 4 out of 5times, the condition “R1-1” is satisfied, and the information processingdevice 12 determines that there is a high possibility of the redundantconfiguration.

When the ratio P_(t) is less than 30% in 4 out of 5 times, or the ratioP_(t) is less than 10% in 2 out of 5 times, the condition “R1-2” issatisfied, and the information processing device 12 determines thatthere is a medium possibility of the redundant configuration.

In any other cases, the condition “R1-3” is satisfied, and theinformation processing device 12 determines that there is a lowpossibility of the redundant configuration.

Second Example

FIG. 9A is a schematic diagram illustrating a determination methodaccording to the second example. In this example, it is assumed a casewhere there are the two virtual machines VM1 and VM2 under the controlof the single virtual machine VM3 in the service A. In this case, theinformation processing device 12 monitors an input traffic volume tr₃input to the virtual machine VM3 and output traffic volumes tr₁ and tr₂output from the virtual machine VM3 to the virtual machines VM1 and VM2.

If the virtual machines VM1 and VM2 have the redundant configuration,the virtual machine VM3 has the high possibility to function as the loadbalancer that distributes traffic to the virtual machines VM1 and VM2.In this case, virtual machine VM3 distributes input data to its ownvirtual machine to each virtual machine VM1 and VM2 at random in time.Therefore, a total value mot of the output traffic volumes tr₁ and tr₂is equal to the input traffic volume tr₃.

Therefore, in this example, the information processing device 12monitors the traffic volumes tr₁, tr₂, and tr₃ to determine the degreeof possibility that the virtual machines VM1 and VM2 have the redundantconfiguration.

FIG. 9B is a schematic diagram illustrating a monitoring result of thetraffic volumes tr₁, tr₂, and tr₃.

In this example, the total volumes (bits) of traffic in one minute areset as the traffic volumes tr₁, tr₂ and tr₃. The information processingdevice 12 calculates a difference Δtr_(b) between the total valuetr_(tot) of the output traffic volumes tr₁ and tr₂ and the input trafficvolume tr₃. Further, the information processing device 12 determines thedegree of possibility of the redundant configuration based on a ratioP_(b)(=Δtr_(b)/tr₃) occupied by the difference Δtr_(b) to the inputtraffic volume tr₃.

FIG. 10 is a schematic diagram illustrating a determination rule R2 fordetermining the degree of possibility.

In the determination rule R2, the information processing device 12determines the degree of possibility depending on which of theconditions “R2-1”, “R2-2”, and “R2-3” is satisfied, as illustrated inFIG. 10.

Here, the information processing device 12 measures the traffic volumestr₁, tr₂ and tr₃ 5 times. When the ratio P_(b) is less than 10% in 4 outof 5 times, the condition “R2-1” is satisfied, and the informationprocessing device 12 determines that there is the high possibility ofthe redundant configuration.

When the ratio P_(b) is less than 30% in 4 out of 5 times, or the ratioP_(b) is less than 10% in 2 out of 5 times, the condition “R2-2” issatisfied, and the information processing device 12 determines thatthere is the medium possibility of the redundant configuration.

In any other cases, the condition “R2-1” is satisfied, and theinformation processing device 12 determines that there is the lowpossibility of the redundant configuration.

Third Example

FIG. 11A is a schematic diagram illustrating a determination methodaccording to the third example. In FIG. 11A, it is assumed a case wherethe high possibility that the virtual machines VM1 and VM2 in theservice A have the redundant configuration, by the first example and thesecond example, is determined. In this case, there is a high possibilitythat virtual machines VM4 and VMS in the service A under the control ofthe virtual machines VM1 and VM2 also have the redundant configuration.

In order to confirm this, the information processing device 12 monitorsa traffic volume tr₄ flowing between the virtual machine VM1 and thevirtual machine VM4 and a traffic volume tr₅ flowing between the virtualmachine VM2 and the virtual machine VMS. If the virtual machines VM4 andVMS have the redundant configuration, there should be no largedifference between the traffic volumes tr₄ and tr₅.

Therefore, in this example, the information processing device 12monitors the traffic volumes tr₄ and tr₅ to determine the degree ofpossibility that the virtual machines VM4 and VMS have the redundantconfiguration.

FIG. 11B is a schematic diagram illustrating a monitoring result of thetraffic volumes tr₄ and tr₅.

In this example, the total volumes (bits) of traffic in one minute areset as the traffic volumes tr₄ and tr₅, and the information processingdevice 12 calculates a difference Δtr_(c) between them. Then, theinformation processing device 12 determines the degree of possibility ofthe redundant configuration based on a ratio P_(c)(=Δtr_(c)/tr₄)occupied by the difference Δtr_(c) to the traffic volume tr₄.

FIG. 12 is a schematic diagram illustrating a determination rule R3 fordetermining the degree of possibility.

In the determination rule R3, the information processing device 12determines the degree of possibility depending on which of theconditions “R3-1”, “R3-2”, and “R3-3” is satisfied, as illustrated inFIG. 12.

Here, the information processing device 12 measures the traffic volumestr₄ and tr₅ 5 times. When the ratio P_(c) is less than 10% in 4 out of 5times, the condition “R3-1” is satisfied, and the information processingdevice 12 determines that there is the high possibility of the redundantconfiguration.

When the ratio P_(c) is less than 30% in 4 out of 5 times, or the ratioP_(c) is less than 10% in 2 out of 5 times, the condition “R3-2” issatisfied, and the information processing device 12 determines thatthere is the medium possibility of the redundant configuration.

In any other cases, the condition “R3-3” is satisfied, and theinformation processing device 12 determines that there is the lowpossibility of the redundant configuration.

Fourth Example

FIG. 11A is a schematic diagram illustrating a determination methodaccording to the fourth example. In this example, when the two virtualmachines VM1 and VM2 that provide the service A are connected by anetwork, the information processing device 12 determines a degree ofpossibility that the virtual machines VM1 and VM2 are redundantdatabases. The virtual machines VM1 and VM2 may be deployed in the samecloud location, or may be deployed in different cloud locations.Further, the services B, C, and D may be used instead of the service A.This also applies to the fifth example described later.

When the virtual machines VM1 and VM2 are the redundant databases, acontent F of a disk D1 in the virtual machine VM1 is copied to a disk D2in the virtual machine VM2 after a certain period of time ΔT elapsed. Inthis case, the virtual machine VM2 operates in the same manner as thevirtual machine VM1 after the certain period of time ΔT. Therefore, aresource usage rate U₂ of the virtual machine VM2 should be the same asa resource usage rate U₁ of the virtual machine VM1 at the time goingback by the certain period of time ΔT.

Therefore, in this example, the information processing device 12monitors the resource usage rates U₁ and U₂ to determine the degree ofpossibility that the virtual machines VM1 and VM2 have the redundantconfiguration. Examples of such resource usage rates U₁ and U₂ include aCPU usage rate (%) or a memory usage rate (%) of each of the virtualmachines VM1 and VM2.

FIG. 13B is a schematic diagram illustrating a monitoring result of theresource usage rates U₁ an U₂.

When the virtual machines VM1 and VM2 have the redundant configuration,the graphs of the resource usage rates U1 and U2 have similar shapes asillustrated in FIG. 13B.

In this example, the information processing device 12 calculates adifference ΔU between a value of the resource usage rate U1 at a time t₁and a value of the resource usage rate U2 at a time t₂. Then, theinformation processing device 12 determines the degree of possibility ofthe redundant configuration based on a ratio P_(u)(=ΔU/U₁) occupied bythe difference ΔU to the resource usage rate U₁.

FIG. 14 is a schematic diagram illustrating a determination rule R4 fordetermining the degree of possibility.

In the determination rule R4, the information processing device 12determines the degree of possibility depending on which of theconditions “R4-1”, “R4-2”, and “R4-3” is satisfied, as illustrated inFIG. 14.

In this example, the information processing device 12 measures thedifference ΔU 5 times at different times. In this case, in the n-thmeasurement, the information processing device 12 measures thedifference ΔU between the resource usage rate U₁ at a time t_(n) and theresource usage rate U₂ at a time t_(n+1). A value of “t_(n)−t_(n+1)” isthe same for all n, and “t_(n)−t_(n+1)” is expressed as ΔT(t_(n)−t_(n+1)−ΔT).

When the ratio P_(u) is less than 10% in 4 or more out of 5 times, thecondition “R4-1” is satisfied, and the information processing device 12determines that there is the high possibility that the virtual machinesVM1 and VM2 have the redundant configuration.

When the ratio P_(u) is less than 30% in 4 or more out of 5 times, orthe ratio P_(u) is less than 10% in 2 or more out of 5 times, thecondition “R4-2” is satisfied, and the information processing device 12determines that there is the medium possibility of the redundantconfiguration.

In any other cases, the condition “R4-3” is satisfied, and theinformation processing device 12 determines that there is the lowpossibility of the redundant configuration.

Fifth Example

FIG. 15A is a schematic diagram illustrating a determination methodaccording to the fifth example. In FIG. 15A, it is assumed a case wherethe high possibility that the virtual machines VM1 and VM2 in theservice A have the redundant configuration, by the fourth example, isdetermined. In this case, there is a high possibility that virtualmachines VM4 and VM5 in the service A which are connected to the virtualmachines VM1 and VM2, respectively, also have the redundant.

In order to confirm this, the information processing device 12 monitorsthe traffic volume tr₄ flowing between the virtual machine VM1 and thevirtual machine VM4 and the traffic volume tr₅ flowing between thevirtual machine VM2 and the virtual machine VM5. If the virtual machinesVM4 and VM5 have the redundant configuration, there should be no largedifference between the traffic volumes tr₄ and tr₅.

Therefore, in this example, the information processing device 12monitors the traffic volumes tr₄ and tr₅ to determine the degree ofpossibility that the virtual machines VM4 and VMS have the redundantconfiguration.

FIG. 15B is a schematic diagram illustrating a monitoring result of thetraffic volumes tr₄ and tr₅.

In this example, the total volumes (bits) of traffic in one minute areset as the traffic volumes tr₄ and t₅, and the information processingdevice 12 calculates a difference Δtr_(d) between them. Then, theinformation processing device 12 determines the degree of possibility ofthe redundant configuration based on a ratio P_(d)(=Δtr_(d)/tr₄)occupied by the difference Δtr_(d) to the traffic volume tr₄.

FIG. 16 is a schematic diagram illustrating a determination rule R5 fordetermining the degree of possibility.

In the determination rule R5, the information processing device 12determines the degree of possibility depending on which of theconditions “R5-1”, “R5-2”, and “R5-3” is satisfied, as illustrated inFIG. 16.

Here, the information processing device 12 measures the traffic volumestr₄ and tr₅ 5 times. When the ratio P_(d) is less than 10% in 4 out of 5times, the condition “R5-1” is satisfied, and the information processingdevice 12 determines that there is the high possibility of the redundantconfiguration.

When the ratio P_(d) is less than 30% in 4 out of 5 times, or the ratio_(d) is less than 10% in 2 out of 5 times, the condition “R5-2” issatisfied, and the information processing device 12 determines thatthere is the medium possibility of the redundant configuration.

In any other cases, the condition “R5-3” is satisfied, and theinformation processing device 12 determines that there is the lowpossibility of the redundant configuration.

Next, a description is given of the functional configuration of theinformation processing device 12. FIG. 17 is a functional configurationdiagram of the information processing device 12. As illustrated in FIG.17, the information processing device 12 includes a communication unit21, a display unit 22, a storage unit 23, and a control unit 24.

The communication unit 21 is a communication interface for connectingits own device to the network 11 (see FIG. 5). The display unit 22 is aprocessing unit that displays a calculation result of a differencebetween the usage charges before and after the migration of the virtualmachine, and is realized by a liquid crystal display device, forexample.

The storage unit 23 is a processing unit realized by, for example, anHDD (Hard Disk Drive), a memory or the like, and stores theabove-mentioned determination rules R1 to R5.

The control unit 24 is a processing unit that controls its own device,and includes a detection unit 25, an extraction unit 26, a determinationunit 27, a calculation unit 28, a display processing unit 29, a trafficmonitoring unit 30 and a resource monitoring unit 31.

The detection unit 25 is a processing unit that detects that the usagecharge of each of the virtual machines VM provided by the cloudproviders is reduced by periodically referring to the charge information15 (FIG. 5) of the cloud providers X, Y, and Z.

Further, the detection unit 25 stores virtual machine informationrelated to the virtual machine VM whose usage charge is reduced, in thestorage unit 23.

FIG. 18 is a diagram illustrating an example of the virtual machineinformation. FIG. 18 illustrates a case where the usage charge of eachof the virtual machines VM is reduced in the two cloud locations R(=(Z,USA)) and V(=(Z, UK)).

In this case, the detection unit 25 stores the information related tothe virtual machines VM of the cloud locations R and V for which theusage charge is reduced, in the virtual machine information. Forexample, an item “status” indicating that the usage charge is updatedstores “update”. An item “cloud provider” stores the cloud provider “Z”that reduced the usage charge. An item “type” stores “nano” which is atype of the virtual machine VM provided by the cloud provider “Z”. Anitem “region” stores the regions “USA” and “UK” where the virtualmachines VM whose usage charges are reduced are deployed. An item“number of CPU” stores the number of virtual CPUs in the virtual machinewhose usage charge is reduced.

An item “usage charge” stores the usage charge of the virtual machine VMafter the charge reduction. Furthermore, an item “OS” stores a name ofan OS to be run by the virtual machine VM after the charge reduction.

Referring to FIG. 17 again, the extraction unit 26 extracts a pluralityof virtual machines VM having attributes similar to those of theplurality of virtual machines whose usage charges are reduced detectedby the detection unit 25, from all the virtual machines VM that providethe service A. One of the attributes is the number of virtual CPUs thatthe virtual machine VM includes. When the number of virtual CPUs in thetwo virtual machines are the same as each other, it is determined thatthe attributes of the virtual machines are similar to each other. Acapacity of a virtual memory in the virtual machine VM may be adopted asthe attribute. In addition, the extraction unit 26 extracts the virtualmachines VM for the services B, C, and D in the same way.

The determination unit 27 is a processing unit that determines thedegree of possibility that the virtual machine VM having the redundantconfiguration exists in the plurality of virtual machines VM thatprovide the services. As an example, the determination unit 27determines the degree of possibility that each of the plurality ofvirtual machine VMs extracted by the extraction unit 26 has theredundant configuration, according to any of the above-mentioneddetermination rules R1 to R5. At the time of the determination, thedetermination unit 27 uses a monitoring result of either the trafficmonitoring unit 30 or the resource monitoring unit 31 described later.

For example, it is assumed that the virtual machines extracted by theextraction unit 26 are the virtual machines VM1 and VM2 in the serviceA. In this case, the determination unit 27 identifies the virtualmachine VM3 connected to each of the virtual machines VM1 and VM2 asillustrated in FIG. 7A. Then, the determination unit 27 determines thedegree of possibility that the virtual machines VM1 and VM2 have theredundant configuration, based on the traffic volumes tr₁ and tr₂ andthe determination rule R1 (FIG. 8). This determination is executed bythe determination unit 27 for each of the services A, B, C, and D.

The calculation unit 28 is a processing unit that calculates adifference between the usage charges before and after the migration whena first virtual machine among the plurality of virtual machines ismigrated to another cloud location different from the cloud locationwhere the first virtual machine is deployed.

FIG. 19 is a schematic diagram illustrating an example of deploymentdestinations of the virtual machines to be calculated.

Hereinafter, a description is given of a case of calculating the usagecharges before and after the migration of the virtual machine VM forproviding the service A as an example. In this case, it is assumed thatthe virtual machines extracted by the extraction unit 26 are the virtualmachine VM1 in the cloud location P and the virtual machine VM2 in thecloud location Q. Further, it is assumed that the virtual machines whoseusage charges are reduced are virtual machines VM6 and VM7 in the cloudlocation R. In this case, the calculation unit 28 calculates the usagecharge of the service A when the virtual machine VM1 is migrated to thevirtual machine VM6 and the virtual machine VM2 is migrated to thevirtual machine VM7, and calculates a difference between calculatedusage charge and the usage charge before the migration. This calculationis executed by the calculation unit 28 for each of the services A, B, C,and D.

Referring to FIG. 17 again, the display processing unit 29 is aprocessing unit that executes a process of displaying the calculationresult by the calculation unit 28 and the determination result by thedetermination unit 27 on the display unit 22.

The traffic monitoring unit 30 is a processing unit that monitors eachof the traffic volumes tr₁, tr₂, tr₃, tr₄ and tr₅ in FIGS. 7A, 9A, 11Aand 15A. The resource monitoring unit 31 is a processing unit thatmonitors the resource usage rates U₁ and U₂ in FIG. 13B.

Next, a description is given of a charge calculation method according tothe present embodiment. FIG. 20 is a flowchart of the charge calculationmethod according to the present embodiment. First, the detection unit 25detects that the usage charge of the virtual machine provided by any ofthe cloud providers X, Y, and Z is reduced by referring to the chargeinformation 15 (step S11). Then, the detection unit 25 stores thevirtual machine information (see FIG. 18) related to the virtual machinewhose usage charge is reduced, in the storage unit 23.

Next, the extraction unit 26 extracts the plurality of virtual machinesVM having attributes similar to those of the virtual machines whoseusage charges are reduced detected in step S11, from all the virtualmachines VM that provide the service A (step S12).

For example, it is assumed that the virtual machines in which thereduction of the usage charge detected are the virtual machines VM6 andVM7 in the cloud location R (see FIG. 19). In this case, the extractionunit 26 extracts the plurality of virtual machines VM each having thesame number of virtual CPUs as any of the virtual machines VM6 and VM7from all the virtual machine VMs that provide the service A. Thecapacity of the virtual memory may be used instead of the number ofvirtual CPUs. Further, the extraction unit 26 extracts the plurality ofvirtual machines VM for the services B, C, and D in the same way.

Next, the traffic monitoring unit 30 monitors the traffic volumes tr1,tr2, tr3, tr4 and tr5 (see FIGS. 7A, 9A, 11A and 15A) of the respectiveextracted virtual machines VM (step S13).

Further, the resource monitoring unit 31 monitors the resource usagerates U₁ and U₂ (see FIG. 13B) of the respective extracted virtualmachines VM (step S14).

Next, the determination unit 27 performs a determination process fordetermining the degree of possibility that the virtual machine havingthe redundant configuration exist in the plurality of virtual machinesproviding the service (step S15). Here, the determination unit 27determines the degree of possibility that the respective extractedvirtual machines VM have the redundant configuration based on any of theabove-mentioned determination rules R1 to R5. The details of thedetermination process are described later.

Next, the calculation unit 28 sorts the plurality of services A, B, C,and D (step S16).

FIG. 21 is a schematic diagram illustrating a sorting method. A ratioZ_(A) in FIG. 21 indicates a ratio of the number of virtual machineshaving attributes similar to those of the virtual machines whose usagecharges are reduced, to the number of all virtual machines VM includedin the service A. The same applies to the respective ratios Z_(B),Z_(C), and Z_(D) of other services B, C, and D.

Before sorting, the service A has the highest ratio occupied by thevirtual machine whose usage charge is reduced.

In this embodiment, each of the ratios Z_(A), Z_(B), Z_(C), and Z_(D) ismultiplied by a coefficient α corresponding to the degree of possibilitythat the plurality of virtual machines VM having similar attributes tothe virtual machine whose usage charge is reduced have the redundantconfiguration. The coefficient α is a coefficient that decreases as thedegree of possibility of the redundant configuration increases. Here,the coefficient α is set to 0.1 (α=0.1) when the possibility of theredundant configuration is high, and the coefficient α is set to 0.5(α=0.5) when the possibility of the redundant configuration is medium.Moreover, the coefficient α is set to 1 (α=1) when the possibility ofthe redundant configuration is low.

Here, it is assumed that the service A has a high possibility ofincluding the virtual machine VM having the redundant configuration andthe coefficient α is set to 0.1 (α=0.1). It is assumed that each of theservices C and D has a medium possibility of including the virtualmachine VM having the redundant configuration and the coefficient α isset to 0.5 (α=0.5). Further, it is assumed that each of the services Cand D has a low possibility of including the virtual machine VM havingthe redundant configuration and the coefficient α is set to 1 (α=1).

The calculation unit 28 multiplies each of the ratios ZA, ZB, ZC, and ZDby the coefficient α, and sorts the respective services in a descendingorder of the multiplication results. In this example, the service B hasthe largest multiplication result, and the services C, D, and A havesmaller multiplication results in that order. The sorting method is notlimited to this. FIG. 22A is a schematic diagram illustrating anothersorting method.

In this example, the services A, B, C, and D are sorted in an ascendingorder of the degree of possibility of the redundant configurationdetermined in step S15. For example, it is assumed that the possibilityof redundant configuration is high in the services A and C, medium inthe service D, and low in the service B, as illustrated in FIG. 22A.

In this case, when the calculation unit 28 sorts the services, theservices B, D, A and C are arranged in this order (B->D->A->C).

FIG. 22B is a schematic diagram illustrating still another sortingmethod. In this example, the services A, B, C, and D are sorted in thedescending order of the total number of resources included in therespective virtual machines VM extracted in step S12. The total numberof resources that serve as a basis for sorting is the total number ofvirtual CPUs in all the extracted virtual machines VM. Further, a totalcapacity of the virtual memories in all the extracted virtual machinesVM may be adopted as the total number of resources.

In case of FIG. 22B, when the calculation unit 28 sorts the services,the services D, B, C and A are arranged in this order (D->B->C->A).

Referring to FIG. 20 again, the calculation unit 28 calculates thedifference between the usage charges of the service before and after themigration of the virtual machine with reference to the chargeinformation 15 (step S17). Here, the calculation unit 28 calculates thedifference between the usage charges of the service before and after themigration, based on the charge information 15, when the respectivevirtual machines VM extracted in step S12 are migrated to the virtualmachines VM after the charge reduction.

At this time, the calculation unit 28 calculates the difference betweenthe usage charges in order from the beginning of the services sorted asillustrated in FIG. 21. In the example of FIG. 21, a calculation orderof the difference between the usage charges is an order of the serviceB, the service C, the service D and the service A.

The calculation process of step S17 terminates without calculating thedifference between the usage charges for subsequent services when adifference between the calculated usage charge and the current usagecharge is not equal to or less than a predetermined charge. For example,the calculation unit 28 terminates the calculation when it is determinedthat a migration cost required to migrate the respective virtualmachines VM to the virtual machines whose usage charges are reduced islarger than the difference. This avoids a situation where the chargereduction for the respective virtual machines VM is offset by themigration cost, resulting in a higher charge for the service.

In the present embodiment, as a result of the sorting as illustrated inFIG. 21, the calculation is performed in order from the service thatincludes many virtual machines VM having attributes similar to those ofthe virtual machines whose usage charges are reduced, and has a lowpossibility that the many virtual machines VM have the redundantconfiguration. Therefore, the usage charge can be greatly reduced bymigrating the virtual machines VM. Further, since the many virtualmachines VM do not have the redundant configuration, it is possible topreferentially calculate the service that the user of the service caneasily accept the migration.

On the other hand, with respect to the service where there are fewvirtual machines VM having attributes similar to those of the virtualmachines whose usage charges are reduced and the charge reduction cannotbe expected so much, the calculation is likely to be terminated, andunnecessary calculation can be avoided. Similarly, with respect to theservice where there is a high possibility that the respective virtualmachines VM have the redundant configuration and it is expected that theuser of the service hardly accepts the migration of the virtual machinesVM, the calculation is terminated and the unnecessary calculation can beavoided.

Similarly, also when the sorting is performed based on the degree ofpossibility of the redundant configuration as illustrated in FIG. 22A,the calculation for the service having a high possibility of theredundant configuration is likely to be terminated, and the unnecessarycalculation can be avoided.

When the sorting is performed based on the total number of resources asillustrated in FIG. 22B, the calculation is performed in order from theservice provided by the virtual machines having a large total number ofresources and a high usage charge. Therefore, the calculation isperformed in order from the service with the large charge reduction dueto the migration of the virtual machines, and the unnecessarycalculation for the service with the small charge reduction that isdifficult for the user to accept can be terminated.

Then, the display processing unit 29 performs a process of displayingthe degree of possibility that the virtual machine having the redundantconfiguration determined in step S15 exists and the calculation resultof the difference between the usage charges in step S17 on the displayunit 22 (step S18). This completes basic steps in the charge calculationmethod according to the present embodiment.

FIG. 23 is a schematic diagram illustrating an example of screen displayof the display unit 22 in step S18.

In this example, the display processing unit 29 causes the display unit22 to display a rate of the charge reduction from the usage chargebefore the migration in an identifiable manner, as the differencebetween the usage charges of the service before and after the migrationof the virtual machines. The display processing unit 29 may display theusage charge after the charge reduction on the display unit 22 insteadof the difference between the usage charges.

The display processing unit 29 causes characters “high”, “medium” and“low” to be displayed on the display unit 22 in the identifiable manneras the degree of the possibility that the virtual machines having theredundant configuration exist in the current respective virtual machinesVM.

Thereby, the administrator N of the services A, B, C, and D can confirmthe degree of possibility that the virtual machines VM having theredundant configuration exist in each service, and the differencebetween the usage charges. As a result, the administrator N can predictwhether the user of the service accepts the migration to another virtualmachine whose usage charge is reduced, and then determine whether topresent the usage charge after the charge reduction to the user.

For example, the service A has a maximum charge reduction rate comparedto other services, but on the other hand, there is a high possibilitythat the current virtual machine VM having the attribute similar to thatof other virtual machine whose usage charge is reduced has the redundantconfiguration. If the virtual machine VM is migrated to the othervirtual machine whose usage charge is reduced in the same cloudlocation, the virtual machines VM having the redundant configuration aredeployed in the same cloud location. In this case, each virtual machineVM cannot be used when the failure occurs in the above-mentioned cloudlocation, and hence there is no sense in making each virtual machine VMredundant. Therefore, the administrator N can predict that the user ofthe service A prevents the migration of the virtual machine, and canrefrain from proposing the migration of the virtual machine to the userof service A.

Moreover, since it is possible to prevent the administrator N fromaccidentally migrating the virtual machine VM having the redundantconfiguration to the same cloud location, it is also possible to obtaina technical improvement that the availability of service A can beprevented from decreasing.

On the contrary, the service B has a small price reduction rate, butthere is a low possibility that the virtual machine VM having theattribute similar to that of the virtual machine whose usage charge isreduced has the redundant configuration. Therefore, even if the virtualmachine VM is migrated to the same cloud location where the usage chargeis reduced, it is unlikely that the user of the service B is concernedabout the redundant configuration of the virtual machine VM to preventthe migration. As a result, the administrator N can positively proposeto the user of the service B to migrate the virtual machine.

Next, a description is given of the determination process in step S15.This determination process can be performed by any one of the followingfirst to fifth examples.

FIRST EXAMPLE

FIG. 24 is a flowchart of the determination process according to thefirst example. In this example, the degree of possibility of theredundant configuration is determined by using the traffic volumes tr₁and tr₂ illustrated in FIGS. 7A and 7B.

First, the determination unit 27 calculates the difference Δtr_(a)between the traffic volumes tr₁ and tr₂ monitored by the trafficmonitoring unit 30 (step S21), and further calculates the ratioP_(t)(=Δtr_(a)/tr₁).

Next, the determination unit 27 determines whether the ratio P_(t)satisfies the condition “R1-1” of the determination rule R1 (see FIG. 8)(step S22). When the ratio P_(t) satisfies the condition “R1-1” (stepS22: YES), the process proceeds to step S23, and the determination unit27 determines that the degree of possibility of the redundantconfiguration is “high”.

When the ratio P_(t) does not satisfy the condition “R1-1” (step S22:NO), the process proceeds to step S24. The determination unit 27determines whether the ratio P_(t) satisfies the condition “R1-2” of thedetermination rule R1 (step S24). When the ratio P_(t) satisfies thecondition “R1-2” (step S24: YES), the process proceeds to step S25, andthe determination unit 27 determines that the degree of possibility ofthe redundant configuration is “medium”.

When the ratio P_(t) does not satisfy the condition “R1-2” (step S24:NO), the process proceeds to step S26. In step S26, the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “low”. After this, the process returns to S16 of FIG.20.

SECOND EXAMPLE

FIG. 25 is a flowchart of the determination process according to thesecond example. In this example, the degree of possibility of theredundant configuration is determined by using the traffic volumes tr₁,tr₂ and tr₃ illustrated in FIGS. 9A and 9B.

First, the determination unit 27 calculates the difference Δtr_(b)between the total value tr_(tot), of the output traffic volumes tr₁ andtr₂ and the input traffic amount tr₃ which are monitored by the trafficmonitoring unit 30 (step S31), and further calculates the ratioP_(b)(=Δtr_(b)/tr₃).

Next, the determination unit 27 determines whether the ratio P_(b)satisfies the condition “R2-1” of the determination rule R2 (see FIG.10) (step S32). When the ratio P_(b) satisfies the condition “R2-1”(step S32: YES), the process proceeds to step S33, and the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “high”.

When the ratio P_(b) does not satisfy the condition “R2-1” (step S32:NO), the process proceeds to step S34. The determination unit 27determines whether the ratio P_(b) satisfies the condition “R2-2” of thedetermination rule R2 (step S34). When the ratio P_(b) satisfies thecondition “R2-2” (step S34: YES), the process proceeds to step S35, andthe determination unit 27 determines that the degree of possibility ofthe redundant configuration is “medium”.

When the ratio P_(b) does not satisfy the condition “R2-2” (step S34:NO), the process proceeds to step S36. In step S36, the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “low”. After this, the process returns to S16 of FIG.20.

THIRD EXAMPLE

FIG. 26 is a flowchart of the determination process according to thethird example. In this example, the degree of possibility of theredundant configuration is determined by using the traffic volumes tr₄and tr_(s) illustrated in FIGS. 11A and 11B.

First, the determination unit 27 calculates the difference Δtr_(c)between the traffic volumes tr₄ and tr₅ monitored by the trafficmonitoring unit 30 (step S41), and further calculates the ratioP_(c)(=Δtr_(c)/tr₄).

Next, the determination unit 27 determines whether the ratio P_(c)satisfies the condition “R3-1” of the determination rule R3 (see FIG.12) (step S42). When the ratio P_(c) satisfies the condition “R3-1”(step S42: YES), the process proceeds to step S43, and the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “high”.

When the ratio P_(c) does not satisfy the condition “R3-1” (step S42:NO), the process proceeds to step S44. The determination unit 27determines whether the ratio P_(c) satisfies the condition “R3-2” of thedetermination rule R3 (step S44). When the ratio P_(c) satisfies thecondition “R3-2” (step S24: YES), the process proceeds to step S45, andthe determination unit 27 determines that the degree of possibility ofthe redundant configuration is “medium”.

When the ratio P_(c) does not satisfy the condition “R3-2” (step S44:NO), the process proceeds to step S46. In step S46, the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “low”. After this, the process returns to S16 of FIG.20.

FOURTH EXAMPLE

FIG. 27 is a flowchart of the determination process according to thefourth example. In this example, the degree of possibility of theredundant configuration is determined by using the resource usage ratesU₁ and U₂ illustrated in FIG. 13B.

First, the determination unit 27 calculates the difference ΔU betweenthe resource usage rate U₁ and the resource usage rate U₂ monitored bythe resource monitoring unit 31 (step S51), and further calculates theratio Pu(=ΔU/U₁). In the difference ΔU, the resource usage rate U₁ is avalue at the time t₁, and the resource usage rate U₂ is a value at thetime t₂.

Next, the determination unit 27 determines whether the ratio P_(u)satisfies the condition “R4-1” of the determination rule R4 (see FIG.14) (step S52). When the ratio P_(u) satisfies the condition “R4-1”(step S52: YES), the process proceeds to step S53, and the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “high”.

When the ratio P_(u) does not satisfy the condition “R4-1” (step S52:NO), the process proceeds to step S54. The determination unit 27determines whether the ratio P_(u) satisfies the condition “R4-2” of thedetermination rule R4 (step S54). When the ratio P_(u) satisfies thecondition “R4-2” (step S54: YES), the process proceeds to step S55, andthe determination unit 27 determines that the degree of possibility ofthe redundant configuration is “medium”.

When the ratio P_(u) does not satisfy the condition “R4-2” (step S54:NO), the process proceeds to step S56. In step S56, the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “low”. After this, the process returns to S16 of FIG.20.

FIFTH EXAMPLE

FIG. 28 is a flowchart of the determination process according to thefifth example. In this example, the degree of possibility of theredundant configuration is determined by using the traffic volumes tr₄and tr₅ illustrated in FIGS. 15A and 15B.

First, the determination unit 27 calculates the difference Δtr_(d)between the traffic volumes tr₄ and tr₅ monitored by the trafficmonitoring unit 30 (step S61), and further calculates the ratioP_(d)(=Δtr_(d)/tr₄).

Next, the determination unit 27 determines whether the ratio P_(d)satisfies the condition “R5-1” of the determination rule R5 (see FIG.16) (step S62). When the ratio P_(d) satisfies the condition “R5-1”(step S62: YES), the process proceeds to step S63, and the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “high”.

When the ratio P_(d) does not satisfy the condition “R5-1” (step S62:NO), the process proceeds to step S64. The determination unit 27determines whether the ratio P_(d) satisfies the condition “R5-2” of thedetermination rule R5 (step S64). When the ratio P_(d) satisfies thecondition “R5-2” (step S64: YES), the process proceeds to step S65, andthe determination unit 27 determines that the degree of possibility ofthe redundant configuration is “medium”.

When the ratio P_(d) does not satisfy the condition “R5-2” (step S64:NO), the process proceeds to step S66. In step S66, the determinationunit 27 determines that the degree of possibility of the redundantconfiguration is “low”. After this, the process returns to S16 of FIG.20.

Hardware Configuration

Next, a description is given of hardware configuration of theinformation processing device 12 according to the present embodiment.

FIG. 29 is a hardware configuration diagram of the informationprocessing device 12 according to the present embodiment.

As illustrated in FIG. 29, the information processing device 12 includesa storage device 12 a, a memory 12 b, a processor 12 c, a communicationinterface 12 d, a display device 12 e, and an input device 12 f. Theseelements are connected to each other by a bus 12 g.

The storage device 12 a is a non-volatile storage such as an HDD (HardDisk Drive) or an SSD (Solid State Drive), and stores a chargecalculation program 40 according to the present embodiment.

The charge calculation program 40 may be recorded on a computer-readablerecording medium 12 h, and the processor 12 c may read the chargecalculation program 40 from the recording medium 12 h.

Examples of such a recording medium 12 h include physically portablerecording media such as a CD-ROM (Compact Disc-Read Only Memory), a DVD(Digital Versatile Disc), and a USB (Universal Serial Bus) memory.Further, a semiconductor memory such as a flash memory, or a hard diskdrive may be used as the recording medium 12 h. The recording medium 12h is not a temporary medium such as a carrier wave having no physicalform.

Further, the charge calculation program 40 may be stored in a deviceconnected to a public line, an Internet, a LAN (Local Area Network), orthe like. In this case, the processor 12 c may read and execute thecharge calculation program 40.

Meanwhile, the memory 12 b is hardware that temporarily stores data,such as a DRAM, and the charge calculation program 40 is deployed on thememory 12 b.

The processor 12 c is hardware such as a CPU (Central Processing Unit)and a GPU (Graphical Processing Unit) that control each part of theinformation processing device 12. Further, the processor 12 c executesthe charge calculation program 40 in cooperation with the memory 12 b.

In this way, the control unit 24 of FIG. 17 is realized by executing thecharge calculation program 40 in cooperation with the memory 12 b andthe processor 12 c. The control unit 24 includes the detection unit 25,the extraction unit 26, the determination unit 27, the calculation unit28, the display processing unit 29, the traffic monitoring unit 30, andthe resource monitoring unit 31, as described above. Further, thestorage unit 23 of FIG. 17 is realized by the storage device 12 a andthe memory 12 b.

Further, the communication interface 12 d is a communication interfacesuch as a NIC (Network Interface Card) for connecting the informationprocessing device 12 to the network 11. The communication unit 21 ofFIG. 17 is realized by the communication interface 12 d.

The display device 12 e is hardware such as a liquid crystal displaydevice for realizing the display unit 22 of FIG. 17. The calculationresult of FIG. 23 and the possibility of the redundant configuration aredisplayed on the display device 12 e. The input device 12 f is hardwaresuch as a keyboard and a mouse. For example, the administrator issuesvarious instructions to the information processing device 12 byoperating the input device 12 f.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments of the presentinvention have been described in detail, it should be understood thatthe various change, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. A charge calculation method executed by a processor included in a computer to execute a process, the process comprising: determining a degree of possibility that a virtual machine having redundant configuration exists in a plurality of virtual machines that provide a service; calculating a difference of charges for using the service before and after a first virtual machine among the plurality of virtual machines is migrated to an another location different from a location where the first virtual machine is located; and displaying the degree of possibility and the difference with respect to the service.
 2. The charge calculation method as claimed in claim 1, wherein the service is provided in plural, and the process further comprising: determining the degree of the possibility for each of the services; calculating the difference of each of the services, where the calculating is performed from the service having lower possibility to the service having higher possibility; and stopping the calculating for the service in which the difference is not equal to or less than a predetermined charge.
 3. The charge calculation method as claimed in claim I, wherein the service is provided in plural, and the process further comprising: determining the degree of the possibility for each of the services; and multiplying a ratio of a number of the plurality of virtual machines to a total number of all the virtual machines included in the service by a coefficient that decreases as the degree of the possibility increases; calculating the difference of each of the services, where the calculating is performed from the service having a larger multiplication result of the ratio and the coefficient to the service having a lower multiplication result of the ratio and the coefficient; and stopping the calculating for subsequent services in which the difference is not equal to or less than a predetermined charge.
 4. The charge calculation method as claimed in claim 1, wherein the service is provided in plural, and the process further comprising: calculating the difference of each of the services, where the calculating is performed from the service in which a total number of resources included in the plurality of virtual machines is larger, to the service in which a total number of resources included in the plurality of virtual machines is smaller.
 5. The charge calculation method as claimed in claim 1, the process further comprising: detecting a reduction in a charge for using an another virtual machine, where the another virtual machine is deployed in the another location and has an attribute similar to an attribute of the first virtual machine, by referring to a charge information indicating the charge for using the another virtual machine; wherein the calculating is performed when the reduction in the charge for using the another virtual machine is detected.
 6. The charge calculation method as claimed in claim 5, wherein the attribute is any one of a number of virtual CPUs and a capacity of virtual memory in the virtual machine.
 7. The charge calculation method as claimed in claim 1, the process further comprising: monitoring a first traffic volume input to the first virtual machine within a first predetermined time; monitoring a second traffic volume input to a second virtual machine among the plurality of virtual machines within the first predetermined time; and determining a degree of possibility that the first virtual machine and the second virtual machine have the redundant configuration, based on a difference between the first traffic volume and the second traffic volume.
 8. The charge calculation method as claimed in claim 1, the process further comprising: monitoring a first resource usage rate of the first virtual machine at a first time; monitoring a second resource usage rate of a second virtual machine among the plurality of virtual machines at a second time; and determining a degree of possibility that the first virtual machine and the second virtual machine have the redundant configuration, based on a difference between the first resource usage rate and the second resource usage rate.
 9. The charge calculation method as claimed in claim 1, wherein a second virtual machine and a third virtual machine are included in the plurality of virtual machines, and the process further comprising: monitoring an input traffic volume input to the third virtual machine; monitoring a first output traffic volume output from the third virtual machine to the first virtual machine; monitoring a second output traffic volume output from the third virtual machine to the second virtual machine; and determining a degree of possibility that the first virtual machine and the second virtual machine have the redundant configuration, based on a difference between the input traffic volume and a total value of the first output traffic volume and the second output traffic volume.
 10. The charge calculation method as claimed in claim 7, wherein a fourth virtual machine and a fifth virtual machine are included in the plurality of virtual machines, the process further comprising: determining a degree of possibility that the fourth virtual machine and the fifth virtual machine have the redundant configuration, based on a difference between a traffic volume flowing between the fourth virtual machine and the first virtual machine within a second predetermined time, and another traffic volume flowing between the fifth virtual machine and the second virtual machine within the second predetermined time, when it is determined that the degree of possibility that the first virtual machine and the second virtual machine have the redundant configuration is high.
 11. A non-transitory computer-readable recording medium storing a program that causes a processor included in a computer to execute a process, the process comprising: determining a degree of possibility that a virtual machine having redundant configuration exists in a plurality of virtual machines that provide a service; calculating a difference of charges for using the service before and after a first virtual machine among the plurality of virtual machines is migrated to an another location different from a location where the first virtual machine is located; and displaying the degree of possibility and the difference with respect to the service. 